We propose deep learning based communication methods for adaptive-bandwidth transmission of images over wireless channels. We consider the scenario in which images are transmitted progressively in layers over time or frequency, and such layers can be aggregated by receivers in order to increase the quality of their reconstructions. We investigate two scenarios, one in which the layers are sent sequentially, and incrementally contribute to the refinement of a reconstruction, and another in which the layers are independent and can be retrieved in any order. Those scenarios correspond to the well known problems of \textit{successive refinement} and \textit{multiple descriptions}, respectively, in the context of joint source-channel coding (JSCC). We propose DeepJSCC-$l$, an innovative solution that uses convolutional autoencoders, and present three architectures with different complexity trade-offs. To the best of our knowledge, this is the first practical multiple-description JSCC scheme developed and tested for practical information sources and channels. Numerical results show that DeepJSCC-$l$ can learn to transmit the source progressively with negligible losses in the end-to-end performance compared with a single transmission. Moreover, DeepJSCC-$l$ has comparable performance with state of the art digital progressive transmission schemes in the challenging low signal-to-noise ratio (SNR) and small bandwidth regimes, with the additional advantage of graceful degradation with channel SNR.

翻译：我们为无线频道图像的适应性带宽传输提出了基于深层次学习的通信方法。我们考虑了图像在时间或频率上逐步以层次递增的情景,这些层次可以由接收者汇总,以提高其重建质量。我们调查了两种情景,一种是分层按顺序发送,并逐步有助于完善重建,另一种是分层独立并可以按任何顺序检索。这些情景分别与众所周知的在时间或频率上递增图像的问题相对应。我们考虑的是,在联合源网连接(JSCC)的情况下,图像可逐步以层次递增的方式递增。我们建议,DepJSCC-1美元,这是一个创新的解决方案,使用革命性自动编码器,并提出了三种结构,其复杂程度各不相同。据我们所知,这是第一个为实际信息来源和渠道开发和测试的实用性多功能化联合通信中心计划。 数字化结果表明,在SEGJSC-l$的快速传输中,与S-NR-R-S的快速传输模式相比,其最终的递增性性性性性性性性表现,与S-NR-C-S-递增性递增性传输的单一状态相比,具有高度向性递增性递增性递增性运行性性性性性性表现。